Inspired by the positional encoding in Transformer [1]}, we introduce a novel spatial-temporal node encoding for dynamic graph transformers. The proposed node encoding consists of three components, namely diffusion-based spatial encoding, distance-based spatial encoding, and relative temporal encoding. The two terms of spatial encoding represent the structural role of each node from a global and local perspective respectively. The temporal encoding term, differently, provides the temporal information of each element in the substructure node set. To the end, the three encoding terms are fused to be the input node encoding which contains comprehensive spatial-temporal information. In the rest of this subsection, we discuss the three encoding terms sequentially and then introduce the encoding fusion operation.
